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Biwei Huang

45 papers · 2015–2026 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (10) 🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (12) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (43) 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) 🀝 Dynamic Duo (35) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (25) 🧬 Topic Evolution πŸ† Keyword Champion (2) πŸ’Ž Century Club (44) πŸ”₯ Unstoppable (9) ⚑ Prolific Year (19) πŸ—ƒοΈ Keyword Collector (121)

Conferences

NIPS (16) ICLR (10) ICML (7) AAAI (4) IJCAI (3) JMLR (3) CLEAR (2)

Top co-authors

Kun Zhang (36) Mingming Gong (14) Clark Glymour (10) Ignavier Ng (9) Feng Xie (6) Peter Spirtes (5) Xinshuai Dong (5) Bernhard SchΓΆlkopf (4) Songyao Jin (4) Jiji Zhang (4)

Keywords

causal discovery (15) causal inference (8) causal structure (7) latent variable (6) reinforcement learning (4) directed acyclic graph (3) graphical model (3) latent variable model (3) hierarchical model (3) causal model (3) causal orientation (2) unsupervised learning (2) causal relation (2) causal structure learning (2) observational datum (2) markov equivalence class (2) linear system (2) hierarchical structure (2) world model (2) independent noise (2)

Papers

Revisiting Differentiable Structure Learning: Inconsistency of L1 Penalty and Beyond AAAI 2026
MissScore: High-Order Score Estimation in the Presence of Missing Data ICML 2025
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery ICLR 2025
Differentiable Causal Discovery for Latent Hierarchical Causal Models ICLR 2025
Analytic DAG Constraints for Differentiable DAG Learning ICLR 2025
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning ICLR 2025
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations ICLR 2025
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series ICML 2024
Natural Counterfactuals With Necessary Backtracking NIPS 2024
On the Parameter Identifiability of Partially Observed Linear Causal Models NIPS 2024
Learning Discrete Concepts in Latent Hierarchical Models NIPS 2024
Identifiability Analysis of Linear ODE Systems with Hidden Confounders NIPS 2024
Identifying Latent State-Transition Processes for Individualized Reinforcement Learning NIPS 2024
On Causal Discovery in the Presence of Deterministic Relations NIPS 2024
ACAMDA: Improving Data Efficiency in Reinforcement Learning through Guided Counterfactual Data Augmentation AAAI 2024
Structure Learning with Continuous Optimization: A Sober Look and Beyond CLEAR 2024
Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach CLEAR 2024
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables ICLR 2024
Identifiable Latent Polynomial Causal Models through the Lens of Change ICLR 2024
Federated Causal Discovery from Heterogeneous Data ICLR 2024
Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability ICLR 2024
Score-Based Causal Discovery of Latent Variable Causal Models ICML 2024
Optimal Kernel Choice for Score Function-based Causal Discovery ICML 2024
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge IJCAI 2024
Causal-learn: Causal Discovery in Python JMLR 2024
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables JMLR 2024
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach NIPS 2023
Learning World Models with Identifiable Factorization NIPS 2023
Identification of Nonlinear Latent Hierarchical Models NIPS 2023
Generator Identification for Linear SDEs with Additive and Multiplicative Noise NIPS 2023
Action-Sufficient State Representation Learning for Control with Structural Constraints ICML 2022
Latent Hierarchical Causal Structure Discovery with Rank Constraints NIPS 2022
Identification of Linear Non-Gaussian Latent Hierarchical Structure ICML 2022
Factored Adaptation for Non-Stationary Reinforcement Learning NIPS 2022
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning ICLR 2022
DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding AAAI 2021
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets AAAI 2020
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs NIPS 2020
Causal Discovery from Heterogeneous/Nonstationary Data JMLR 2020
Domain Adaptation as a Problem of Inference on Graphical Models NIPS 2020
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models ICML 2019
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering NIPS 2019
Multi-domain Causal Structure Learning in Linear Systems NIPS 2018
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination IJCAI 2017
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment IJCAI 2015